Data Science Seminar

The LTCI data science seminar is a joint seminar between the DBWeb team and the SSA team of the LTCI. It features talks related to machine learning and data management.

Attendance is generally open to the public, feel free to contact us if you are interested in coming. Talks are held at Télécom ParisTech, 46 rue Barrault, Paris, France, métro Corvisart.

June 22, 2017

The seminar takes place from 14h to 16h in Amphi Saphir, and consists of the two following talks:

Talk 1: Pascal Bianchi, Distributed optimization on graphs using operator splitting methods

Abstract: Consider a network of N agents (computing units) having private
objective functions and seeking to find a minimum of the aggregate
objective. The aim is to design iterative algorithms where, at a each
iteration, an agent updates a local estimate of the minimizer based on the
sole knowledge of its private function and the information received from
its neighbors. In this talk, i will first provide an overview of standard
distributed optimization methods. Then, i will explain how recent and
generic results in stochastic optimization can be used in order to design
asynchronous and adaptive distributed optimization algorithms.

Talk 2: Maximilien Danisch, Towards real-world graph algorithmics

Abstract: Real-world graphs (a.k.a. complex networks) are ubiquitous: the web, Facebook,
brain networks, protein interaction networks, etc. Studying these graphs and
solving computational problems on them (say maximum clique, partitioning or
dense subgraph) has applications in many fields. I will first show that the
structure of some real-world graphs is special. In particular, they are not
adversarial and some difficult problems (say NP-hard problems) can be solved
on some huge real-world graphs (say 2G edges or more). I will then present two
works along the lines of “understanding and leveraging the structure of
real-world graphs in order to build better algorithms”: (i) Enumerating all
k-cliques and (ii) Computing the density-friendly graph decomposition. The
latter one has been published in WWW2017.